Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustainability, the book brings together principles of statistics and probability, multivariate analysis, and spatial analysis methods applicable across a variety of science and engineering disciplines. Learn How to Use a Variety of Data Analysis and Statistics Methods Based on the author’s many years of teaching graduate and undergraduate students, this textbook emphasizes hands-on learning. Organized into two parts, it allows greater flexibility using the material in various countries and types of curricula. The first part covers probability, random variables and inferential statistics, applications of regression, time series analysis, and analysis of spatial point patterns. The second part uses matrix algebra to address multidimensional problems. After a review of matrices, it delves into multiple regression, dependent random processes and autoregressive time series, spatial analysis using geostatistics and spatial regression, discriminant analysis, and a variety of multivariate analyses based on eigenvector methods. Build from Fundamental Concepts to Effective Problem Solving Each chapter starts with conceptual and theoretical material to give a firm foundation in how the methods work. Examples and exercises illustrate the applications and demonstrate how to go from concepts to problem solving. Hands-on computer sessions allow students to grasp the practical implications and learn by doing. Throughout, the computer examples and exercises use seeg and RcmdrPlugin.seeg, open-source R packages developed by the author, which help students acquire the skills to implement and conduct analysis and to analyze the results. This self-contained book offers a unified presentation of data analysis methods for more effective problem solving. With clear, easy-to-follow explanations, the book helps students to develop a solid understanding of basic statistical analysis and prepares them for learning the more advanced and specialized methods they will need in their work.

Providing a solid foundation for twenty-first-century scientists and engineers, Data Analysis and Statistics for Geography, Environmental Science, and Engineering guides readers in learning quantitative methodology, including how to implement data analysis methods using open-source software. Given the importance of interdisciplinary work in sustain

Environmental geologists use a wide range of geologic data to solve environmental problems and conflicts. Professionals and academics in this field need to know how to gather information on such diverse conditions as soil type, rock structure, and groundwater flow and then utilize it to understand geological site conditions. Field surveys, maps, well logs, bore holes, ground-penetrating radar, aerial photos, geologic literature, and more help to reveal potential natural hazards in an area or how to remediate contaminated sites. This new workbook presents accessible activities designed to highlight key concepts in environmental geology and give students an idea of what they need to know to join the workforce as an environmental geologist, engineering geologist, geological engineer, or geotechnical engineer. Exercises cover: • Preparation, data collection, and data analysis • Descriptive and engineering properties of earth materials • Basic tools used in conjunction with geoenvironmental investigations • Forces operating on earth materials within the earth • Inanimate forces operating on earth materials at the surface of the earth • Human activities operating on earth materials Each activity encourages students to think critically and develop deeper knowledge of environmental geology.

Emphasizes real-time monitoring as an emerging area for environmental assessment and compliance and covers the fundamentals on how to develop sensors and systems Presents several entirely new topics not featured in the first edition, including remote sensing and GIS, machine learning, weather radar and satellites, groundwater monitoring, spatial analysis, and habitat monitoring Includes applications to many environmental and ecological systems Uses a practical, hands-on approach with the addition of an accompanying lab manual, which students can use to deepen their understanding, based on the author’s 40 years of academic experience

This book is aimed directly at students of geography, particularly those who lack confidence in manipulating numbers. The aim is not to teach the mathematics behind statistical tests, but to focus on the logic, so that students can choose the most appropriate tests, apply them in the most convenient way and make sense of the results. Introductory chapters explain how to use statistical methods and then the tests are arranged according to the type of data that they require. Diagrams are used to guide students toward the most appropriate tests. The focus is on nonparametric methods that make very few assumptions and are appropriate for the kinds of data that many students will collect. Parametric methods, including Student’s t-tests, correlation and regression are also covered. Although aimed directly at geography students at senior undergraduate and graduate level, this book provides an accessible introduction to a wide range of statistical methods and will be of value to students and researchers in allied disciplines including Earth and environmental science, and the social sciences.

Introduction to Renewable Power Systems and the Environment with R showcases the fundamentals of electrical power systems while examining their relationships with the environment. To address the broad range of interrelated problems that come together when generating electricity, this reference guide ties together multiple engineering disciplines with applied sciences. The author merges chapters on thermodynamics, electricity, and environmental systems to make learning fluid and comfortable for students with different backgrounds. Additionally, this book provides users with the opportunity to execute computer examples and exercises that use the open source R system. Functions of the renpow R package have been described and used in this book in the context of specific examples. The author lays out a clear understanding of how electricity is produced around the world and focuses on the shift from carbon-based energy conversions to other forms including renewables. Each energy conversion system is approached both theoretically and practically to provide a comprehensive guide. Electrical circuits are introduced from the simplest circumstances of direct current (DC), progressing to more complex alternating current (AC) circuits, single phase and three-phase, and electromagnetic devices including generators and transformers. Thermodynamics are employed to understand heat engines and a variety of processes in electrochemical energy conversion, such as fuel cells. The book emphasizes the most prevalent renewable energy conversions in use today: hydroelectrical, wind, and solar. This book is an invaluable for students as a resource to help them understand those aspects of environment systems that motivate the development and utilization of renewable power systems technology.

All students and researchers in environmental and biological sciences require statistical methods at some stage of their work. Many have a preconception that statistics are difficult and unpleasant and find that the textbooks available are difficult to understand. Practical Statistics for Environmental and Biological Scientists provides a concise, user-friendly, non-technical introduction to statistics. The book covers planning and designing an experiment, how to analyse and present data, and the limitations and assumptions of each statistical method. The text does not refer to a specific computer package but descriptions of how to carry out the tests and interpret the results are based on the approaches used by most of the commonly used packages, e.g. Excel, MINITAB and SPSS. Formulae are kept to a minimum and relevant examples are included throughout the text.